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Plaintiff-side firms are not releasing the brakes on Lemon Law litigation anytime soon, and are pressing further down on the pedal with less effort thanks to AI, according to Tariq Hafeez, co-founder and president of LegalEase Solutions.
As vehicle sales grow, original equipment manufacturers (OEMs) are confronting a sharp rise in Lemon Law lawsuits. This significant uptick in Lemon Law claims especially in California, where some OEMs are reporting an increase of100% indicate that OEMs will still be fending off the sour taste of Lemon Law disputes throughout 2023.
OEMs willing to embrace the future can make significant strides to curtail the number of Lemon Law matters on their books. To do so, they should consider matching the punches of plaintiff-side law firms with generative AI. Or, in the alternative, take steps to ensure litigation proceeds in a controlled, more efficient manner.
Generative AI-Powered Litigation
A perfect storm of pandemic-era parts shortages and court closures have stalled Lemon Law activity until now. Ironically, the AI revolution is already making Lemon Law litigation more of a hassle for OEMs. How? By giving owners of lemons a quicker, more accessible path to sue automakers.
Blindly pick through several Lemon Law complaints from a pile of California actions, and defense attorneys will find they mostly look the same. The same Song-Beverly provisions are cited, along with the usual express and implied warranty claims. Usually, only the parties, type of car, and fact sections make each complaint distinguishable.
Unfortunately for OEMs, the standardized structure of Lemon Law claims can make them great candidates for automation. Now leveraging generative AI, plaintiff law firms can go beyond basic document automation to creating sophisticated pleading and discovery materials literally at the push of a button. Unsurprisingly, these capabilities are driving up the sheer volume of litigation and associated costs. In fact, some OEMs are attributing the spike of cases in California to the use of AI tools by plaintiff law firms.
In California, plaintiffs can demand legal fees as part of a client’s Lemon Law damages. It’s a strategy that can significantly help plaintiff-side firms drive up case values far beyond what an OEM could spend on a competitive buyback offer. It’s a chief reason OEMs doing business in California face high-volume Lemon Law requests mainly through over-papered deposition requests, multiple complaints with shared case law cites, and copy-and-paste arguments.
How OEMs Can Sweeten the Sour Taste of High-Volume Lemon Law Claims
Automakers facing high-volume claims have several options to try with many of them possible courtesy of currently available AI. These tools can go far beyond the oft-discussed generative tools such as ChatGPT; they also hit upon predictive analytics for addressing strategy before and after Lemon Law claims develop. For OEMs, it all depends on how deep they and their outside counsel want to go into the technology or if they prefer the time-tested approach of court-issued sanctions.
Consider Automating Routine Aspects of Lemon Law Litigation
With the introduction of AI into the pleadings process, plaintiff-side law firms have opened the door to a drawn-out game of ping pong. To hit back and balance the competitive advantages on both sides of the “v.,” in-house departments must find tools to automate their responsive pleadings and other standard papers.
Some aspects of litigation that OEMs could automate on a rapid-fire cadence include answers, deposition notice responses and discovery requests. After all, most Lemon Law claims involve similar witness and records requests. Whether they work with dedicated legal document automation providers or leverage standalone tools that churn out workable litigation templates, OEMs can at least match the cadence of a plaintiff firm’s punches and make life harder for AI-savvy Lemon Law firms.
Use Predictive Analytics To Offer Competitive Buybacks and Settlements
Ultimately, OEMs have major gripes with Lemon Law disputes because they tend to be more drawn out than needed. Of course, how plaintiff attorneys approach these claims has a substantial amount to do with this, particularly given the considerable importance legal fees play in calculating California Lemon Law claim damages.
Ultimately, OEMs and their affiliated dealers are dealing with customers who want a top-notch car to drive in. The closer these companies can buy out suspect lemons and pair buyers with fully operational vehicles, the less likely they’ll confront mounting Lemon Law claims.
OEMs have what they need to calculate competitive buyback prices in the records and data they keep in digital storage. All that’s required is an AI system that the company can train to identify competitive buyback patterns. While vendors can help organize these approaches and train applicable AI tools, an OEM’s in-house tech talent or blue-chip AI and machine-learning hires can accomplish the same.
To generate defensible buyback pricing models, the OEM should have historical data on its previous buybacks over at least the past three years. Having this type of data set would give an OEM’s AI program enough data to account for any wayward variables that could undercut an AI program’s predictions. Whether the OEM uses a provider or its in-house talent, it should pull whatever readable data the company has across its matter management or sales software for organization and training. From there, the provider or in-house team should leverage best machine-learning practices to help the AI identify important patterns. The end goal of these efforts is to generate a rinse-and-repeat algorithm that can identify competitive buyback offers based on the vehicle’s year, model and alleged defects.
With Lemon Law actions, the statute of limitations clock is always ticking giving OEMs a considerable incentive to resolve these matters as soon as possible. Using an AI algorithm to generate competitive settlement offers can be a huge game-changer to address the root cause of most Lemon Law cases before they fester into more costly matters.
Using Predictive Analytics To Predict How Judges Would Rule and Opponents Will Argue
Imagine typing in an AI prompt that asks your legal research program of choice to predict how a California judge would rule on a particular Lemon Law case. Today, this option is possible and it is a tool that OEMs should explore if they navigate the California court system.
The legal research and tech marketplaces offer plenty of options for leveraging AI to track likely judicial decisions and trends. Some tools will provide matrixes that highlight how judges have approached Lemon Law decisions with similar fact patterns. Others will even go a step further into analyzing opposing counsel’s performance and different litigation strategies, along with the various factors defense attorneys should consider when preparing for motions or oral arguments.
When browsing around for analytics tools, OEM departments should ensure that the programs they consider are built on and permit access to state court dockets. As Lemon Law litigation is predominately state-law driven, larger tools that primarily target federal dockets will be less helpful. Still, as California and other states have made their dockets public and crawlable for software, OEMs should take every opportunity to use public Lemon Law docket information to their advantage to create actionable strategies against heightened Lemon Law litigation.
The Importance of Planning Ahead
As simple as Lemon Law claims may sound, they can cause many headaches for OEMs in weak Lemon Law jurisdictions such as California. It is true that OEMs still have the option to request judicial sanctions against plaintiff firms that over paper their cases a strategy that is worth considering for automakers depending on the circumstances. It is also true that the increasing popularity of electric vehicles could rein in this type of litigation, mainly because of the significantly-reduced mechanical parts involved in these cars.
However, as the situation in California currently stands, plaintiff-side firms are not releasing the brakes on Lemon Law litigation anytime soon, and are pressing further down on the pedal with less effort thanks to AI. OEMs should strategize now and assess how to use current iterations of AI to keep up with the pace of and ultimately thwart the efforts of plaintiff-side firms.
Tariq Hafeez is the co-founder and president of LegalEase Solutions. He helps OEM in-house legal and compliance teams leverage legal transformation to improve and streamline how they approach legal research, compliance, contract management, and litigation analytics and support.
Reprinted with permission from the August 28, 2023 issue of The Recorder. © 2023 ALM Media Properties, LLC. Further duplication without permission is prohibited. All rights reserved.
